Analyses:

Annual avereage SWE

  • low swe 2004-2007 and 2018
  • Carrizo always has highest average SWE value, while Navajo mountain usually has the least

The annual average % contribution to the total annual average swe of all 6 mountain ranges:

  • Chuska: 0.81
  • Defiance Plateau 0.11
  • Black Mesa 0.06
  • Carrizo: 0.02
  • Navajo Mt 0.0023
  • Mt Powell 0.0049

Daily SWE variability (November - April Water Year)

Monthly Mean SWE variability (November - April Water Year)

ch_wk_graph <- graph_with_wateryear(ch_weekly, "Chuska", type = geom_col)
ch_wk_graph

All monthly mean

Correlation matrix of all monthly mean swe

- Black Mesa to Mt. Powell, Black Mesa to Defiance Plateau and Defiance Plateau and Mt Powell all have the highest correlations between each other - Least correlated are Black Mesa and Navajo Mt, Navajo Mt and Mt Powell, Navajo Mt and Defiance Plateau

Monthly Median

Median monthly swe for each region for each month of the water year

Monthly Anomalies (November - April Water Year)

Correlations Between locations

Weekly mean correlation November - April

Pearson’s Correlation R squared:

  • Chuska vs Black Mesa: 0.46

  • Carrizo vs Chuska: 0.63

  • Carrizo vs Black Mesa: 0.73

Chuska vs Black Mesa R squared: 0.4634808

Chuska vs Carrizo R squared: 0.631619

Black Mesa vs Carrizo R squared: 0.7250093

Month specific time series

November - April Averaged SWE values

  • Chuska and Defiance plateau frequently have the higest av swe values, navajo mt usually has the least.
  • most everything is melted by april

Total SWE for each region analyses

Timing of Max Weekly Average SWE Value

# what month does the max weeek usually occur for chuska?
mnth_mzx <- ch_max_weekly_yr %>% 
  mutate(month = month(date)) %>% 
  count(month) %>% 
  mutate(perc = n/sum(n))

  • All of the high elevation regions have a negative slope, with Chuska having the most negative slope, except Navajo Mt & Carrizo
  • However, none of the trends are very statistically significant

SWE compared to Suzanne’s SCA

## Error in .f(.x[[i]], ...): object 'water_year' not found
## Error in eval(lhs, parent, parent): object 'ch_sca_month' not found
## Error in eval(lhs, parent, parent): object 'ch_sca_month' not found

SCA Anomaly was scaled down by a factor of 10 to improve analysis

## Error in as.data.frame(y): object 'ch_sca_month' not found
## Error in eval(lhs, parent, parent): object 'chuska_swe_sca' not found
## function (x, y, ...) 
## UseMethod("plot")
## <bytecode: 0x7fb725396388>
## <environment: namespace:graphics>
  • SCA anomalies only somewhat track to SWE anomalies

Make average weekly swe dataframe for all the mountain ranges combined

  • first I am multiplying each region’s swe values by their area
  • then I will add these values up to get a total average amount of snow in the mountains for that week
  • This way when I get an average value, it’s the average amount of snow in the mountains at any one point